Methylating much?

Our readers might have gotten distracted this month by discussions on whether it is right or wrong for Illumina to limit researchers’ use of their kit, and so we are here to help you regain focus: after a deliberately thematic issue on the RBPome, we have just published an accidentally thematic issue on DNA methylation.

Useful tools

This month Genome Biology publishes three tools that many working on DNA methylation should find quite handy.

Mark Robinson (of edgeR, which he published together with another of this issue’s authors, Gordon Smyth) and company present a new method, BayMeth, for the effective quantification of data generated with DNA-methylation-capture-seq techniques (MBD-seq, MeDIP-seq and so on). So if you want to stick to these more cost-efficient methods (than whole-genome bisulfite sequencing, BS-seq) for now, BayMeth is certainly a tool that will give you the perfect excuse to do so. But, as the authors tell us, you should be careful to include spiked methylation controls in your experiments. They also point out that BayMeth can be used as a pre-processing step for differential methylation analysis. Which swiftly takes us to the next tool: MOABS.

MOABS (or model based analysis of bisulfite sequencing data) is developed by Wei Li and colleagues from Baylor College of Medicine and designed specifically to detect differential DNA methylation with as little as 10-fold coverage, and at single CpG resolution. It is, however, not just accurate: it is fast too, and compares favorably to BSmooth, which appeared in our 2012 special issue on epigenomics. MOABS can cut your costs down when you do BS-seq, but it will also help you with differential analysis of the sixth base (5hmC), through combined analysis of both BS-seq and oxBS-seq data.

source: flickr, calciostreaming (CC BY)

Finally, Stephan Beck and colleagues describe a new component of the ChAMP Bioconductor package that will let you use the high-density methylation arrays data for the detection of copy number changes. The researchers show that, when analyzing Infinium HumanMethylation450 BeadChip datasets with their new method, they can detect copy number alterations with the sensitivity of SNP arrays. So if you have an Infinium dataset that you hid in the bottom drawer until better times, take it out now and analyze away!

Variation of methylation

Alongside these new tools, this issue of Genome Biology also includes some interesting research on (perhaps unsurprisingly) variation of DNA methylation.

To kick-off with, a group led by Wolfgang Wagner from Aachen describes a method for tracking aging of blood. The authors do this by measuring DNA methylation at just three CpG sites. Some of our readers will find this reminiscent of Steve Horvath’s article that Genome Biologypublished last year. While the authors did not compare their method with Horvath’s approach in their article, Steve Horvath himself did: and you can read his reply in our comments section. Importantly, while Horvath’s method tends to be more accurate – unsurprisingly, as it uses not three, but over 350 CpG sites – the approach described by Wagner and colleagues is made to be quick, robust and easy to use.

Along the same lines of DNA methylation changes with age, Andrew Jaffe and Rafael Irizarry address yet another issue commonly coming up in DNA methylation studies. That is, that, when studying DNA methylation from blood, the results are often confounded by the cellular heterogeneity of the samples. They find that cellular composition of blood changes with age, and that these changes can explain observed variability of DNA methylation in the datasets they looked at.

While we like DNA methylation as much as the next person, Genome Biology’s February issue is a large one, and full of more amazing science. So whether you are into plants and their lincRNAs, or worms, insects, or humans, we are sure you will find a good story for yourselves.